Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties - Bender, Amelie; Sextro, Walter (Proceedings of the European Conference of the PHM Society 2021)
Predictive Maintenance as a desirable maintenance strategy in industrial applications relies on suitable condition monitoring solutions to reduce costs and risks of the monitored technical systems. In general, those solutions utilize model-based or data-driven methods to diagnose the current state or predict future states of monitored technical systems. However, both methods have their advantages and drawbacks. Combining both methods can improve uncertainty consideration and accuracy. Different combination approaches of those hybrid methods exist to exploit synergy effects. The choice of an appropriate approach depends on different requirements and the goal behind the selection of a hybrid approach. In this work, the hybrid approach for estimating remaining useful lifetime takes potential uncertainties into account. Therefore, a data-driven estimation of new measurements is integrated within a model-based method. To consider uncertainties within the system, a differentiation between different system behavior is realized throughout diverse states of degradation. The developed hybrid prediction approach bases on a particle filtering method combined with a machine learning method, to estimate the remaining useful lifetime of technical systems. Particle filtering as a Monte Carlo simulation technique is suitable to map and propagate uncertainties. Moreover, it is a state-of-the-art model-based method for predicting remaining useful lifetime of technical systems. To integrate uncertainties a multi-model particle filtering approach is employed. In general, resampling as a part of the particle filtering approach has the potential to lead to an accurate prediction. However, in the case where no future measurements are available, it may increase the uncertainty of the prediction. By estimating new measurements, those uncertainties are reduced within the data-driven part of the approach. Hence, both parts of the hybrid approach strive to account for and reduce uncertainties. Rubber-metal-elements are employed as a use-case to evaluate the developed approach. Rubber-metal-elements, which are used to isolate vibrations in various systems, such as railways, trucks and wind turbines, show various uncertainties in their behavior and their degradation. Those uncertainties are caused by diverse inner and outer factors, such as manufacturing influences and operating conditions. By expert knowledge the influences are described, analyzed and if possible reduced. However, the remaining uncertainties are considered within the hybrid prediction method. Relative temperature is the selected measurand to describe the element’s degradation. In lifetime tests, it is measured as the difference between the element’s temperature and the ambient temperature. Thereby, the influence of the ambient temperature on the element’s temperature is taken into account. Those elements show three typical states of degradation that are identified within the temperature measurements. Depending on the particular state of degradation a new measurement is estimated within the hybrid approach to reduce potential uncertainties. Finally, the performance of the developed hybrid method is compared to a model-based method for estimating the remaining useful lifetime of the same elements. Suitable performance indices are implemented to underline the differences between the results.
Keywords: Hybrid prediction method; Multi-model particle filtering; Uncertainty quantification; RUL estimation

PEM fuel cell prognostics using particle filter with model parameter adaptation - Kimotho, James Kuria; Meyer, Tobias; Sextro, Walter (Prognostics and Health Management (PHM), 2014 IEEE Conference on)
Application of prognostics and health management (PHM) in the field of Proton Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing the reliability and availability of these systems. Though a lot of work is currently being conducted to develop PHM systems for fuel cells, various challenges have been encountered including the self-healing effect after characterization as well as accelerated degradation due to dynamic loading, all which make RUL predictions a difficult task. In this study, a prognostic approach based on adaptive particle filter algorithm is proposed. The novelty of the proposed method lies in the introduction of a self-healing factor after each characterization and the adaption of the degradation model parameters to fit to the changing degradation trend. An ensemble of five different state models based on weighted mean is then developed. The results show that the method is effective in estimating the remaining useful life of PEM fuel cells, with majority of the predictions falling within 5% error. The method was employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the winner of the RUL category of the challenge.
Keywords: ageing; particle filtering (numerical methods); proton exchange membrane fuel cells; remaining life assessment; PEM fuel cell prognostics; PHM; RUL predictions; accelerated degradation; adaptive particle filter algorithm; dynamic loading; model parameter adaptation; prognostics and health management; proton exchange membrane fuel cells; remaining useful life estimation; self-healing effect; Adaptation models; Data models; Degradation; Estimation; Fuel cells; Mathematical model; Prognostics and health management

Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters - Windmann, Stefan; Haeb-Umbach, Reinhold (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006))
A marginalized particle filter is proposed for performing single channel speech enhancement with a non-linear dynamic state model. The system consists of a particle filter for tracking line spectral pair (LSP) parameters and a Kalman filter per particle for speech enhancement. The state model for the LSPs has been learnt on clean speech training data. In our approach parameters and speech samples are processed at different time scales by assuming the parameters to be constant for small blocks of data. Further enhancement is obtained by an iteration which can be applied on these small blocks. The experiments show that similar SNR gains are obtained as with the Kalman-LM-iterative algorithm. However better values of the noise level and the log-spectral distance are achieved
Keywords: clean speech training data; iterative methods; iterative speech enhancement; Kalman filter; Kalman filters; Kalman-LM-iterative algorithm; line spectral pair parameters; log-spectral distance; marginalized particle filter; noise level; nonlinear dynamic state speech model; particle filtering (numerical methods); single channel speech enhancement; SNR gains; speech enhancement; speech samples

Robust speaker direction estimation with particle filtering - Warsitz, Ernst; Haeb-Umbach, Reinhold (IEEE Workshop on Multimedia Signal Processing (MMSP 2004))
The paper is concerned with binaural signal processing for a bimodal human-robot interface with hearing and vision. The two microphone signals are processed to obtain an enhanced single-channel input signal for the subsequent speech recognizer and to localize the acoustic source, an important information for establishing a natural human-robot communication. We utilize a robust adaptive algorithm for filter-and-sum beamforming (FSB) and extract speaker direction information from the resulting FIR filter coefficients. Further, particle filtering is applied which conducts a nonlinear Bayesian tracking of speaker movement. Good location accuracy can be achieved even in highly reverberant environments. The results obtained outperform the conventional generalized cross correlation (GCC) method.
Keywords: bimodal human-robot interface; binaural signal processing; enhanced single-channel input signal; filter-and-sum beamforming; filtering theory; FIR filter coefficient; generalized cross correlation method; microphones; microphone signal; nonlinear Bayesian tracking; particle filtering; robust adaptive algorithm; robust speaker direction estimation; signal processing; speech enhancement; speech recognition; speech recognizer; user interfaces